Conventional image and speech processing techniques use complex digital logic. This complex digital logic usually consists of multipliers, adder/subtractors, and shift registers. Such devices are very precise and high precision can be maintained. Digital Signal Processing (DSP) algorithms used for image and speech processing use such devices in the implementation of image processing/image compression, high definition television (HDTV), and various other multimedia applications.
However, very precise arithmetic is not always needed in every speech and image processing/compression application. In some cases, simple analog devices and/or circuits can be used with some amount of signal loss/noise without adversely affecting the result of the processing/compression.
Image and speech processing/compression done in real time (on the fly), also require very high computing throughput and communication bandwidth. This, in turn, requires very high speed and specialized equipment. Since image processing/compression can take advantage of parallelism, a number of digital, parallel processing devices have been designed for processing/compression.
Real-time speech and image processing and compression can be achieved using analog techniques along with high bandwidth operational amplifiers (opamps). Opamps are devices which can be configured to perform multiplication, addition, subtraction, and other arithmetic operations with relative ease and minimal complexity.
FIGS. 1A and 1B illustrate building block used to implement various steps of an image processing/compression algorithm
FIG. 1A illustrates a very general image compression technique currently being used. Image data is captured by a camera/scanner and the sensed image data is digitized. A transformation function (such as a Discrete Cosine Transform (DCT)) is then applied to the digitized data by Transformation Module 10. The coefficients of the transform are then quantized by Quantization Module 12 (i.e., divided by a predetermined number) in that fewer bits are required to code small numbers as compared to coding larger numbers. The quantizer output goes to an Entropy Coding Module 14 which uses, for example, a Huffman coding technique. The resulting processed/compressed data is then stored or transmitted as the compressed image.
The FIG. 1B illustrates the inverse process in which the stored or transmitted data is decompressed to get back the original image data. The system shown in FIG. 1B includes an Entropy Decoding Module 16, an Inverse Quantization Module 18 and an Inverse Transform Module 20.